Summary of Succinct Interaction-aware Explanations, by Sascha Xu et al.
Succinct Interaction-Aware Explanations
by Sascha Xu, Joscha Cüppers, Jilles Vreeken
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed approach combines the strengths of SHAP and NSHAP by partitioning features into parts that significantly interact, allowing for succinct and interpretable additive explanations. This is achieved by deriving a criterion to measure representativeness against complexity and pruning sub-optimal solutions using statistical tests. The resulting explanations are shown to be more accurate and interpretable than those of SHAP and NSHAP on synthetic and real-world data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to explain complex machine learning models better. Right now, we have two ways: SHAP and NSHAP. But both have problems. SHAP doesn’t show feature interactions, while NSHAP shows all features interacting together, which is hard to understand. The new method in this paper tries to combine the best of both worlds by grouping similar features together. It also has a way to measure how good the explanation is and how complex it is. This helps us make better explanations that are easy to understand. |
Keywords
* Artificial intelligence * Machine learning * Pruning
